• Transaction monitoring: Real-time detection of suspicious transactions, structuring (layering), and unusual activity patterns
• Fraud ring detection: Identify networks of coordinated accounts and entities using graph-based network analysis
• KYC/CDD screening: Automate customer onboarding with identity verification, sanctions list matching, and PEP detection
• Customer risk profiling: Assess and continuously monitor customer risk levels; trigger enhanced due diligence when risk escalates
• Typology-based detection: Detect known money laundering schemes (trade-based laundering, cash smuggling, shell companies, placement/layering/integration)
• Sanctions and embargo compliance: Screen against OFAC, UNSC, EU, UK and other sanctions lists in real-time
• Regulatory reporting: Automate Suspicious Activity Reports (SARs) and Currency Transaction Reports (CTRs) with audit trails
• Third-party and correspondent banking risk: Monitor high-risk jurisdictions and correspondent relationships

Financial institutions face severe financial crime and regulatory challenges:

• Regulatory Pressure: FATF Recommendations, FinCEN, EU 5th AML Directive, and national banking authorities require robust AML/KYC programs with escalating penalties for non-compliance (up to $10B+ fines)
• Evolving Threats: Money laundering techniques evolve rapidly; traditional rules-based systems fail to detect novel schemes and emerging typologies
• False Positive Overload: Rule-based systems generate high false positive rates (>95%), overwhelming compliance teams and increasing costs
• Complex Transaction Volumes: Institutions process billions of transactions daily; manual review is impossible; AI-driven automation is essential
• Cross-Border Complexity: International correspondent banking, remittances, and cross-border transfers create network complexity that requires graph-based analysis
• Illicit Finance Scale: Globally, $800B-$2T annually is laundered; terrorist financing, sanctions evasion, and corruption demand sophisticated detection

• Risk mitigation: Detect money laundering, terrorist financing, and fraud before funds reach illicit actors; reduce reputational and legal exposure
• Regulatory compliance: Meet FATF standards, FinCEN, GDPR, PCI-DSS, and banking authority requirements; avoid massive fines and enforcement actions
• Operational efficiency: Reduce false positives from 95%+ to <10%, freeing compliance analysts to focus on genuine threats
• Cost reduction: Automate KYC/CDD, transaction monitoring, and reporting; reduce headcount and operational burden
• Institutional integrity: Protect brand reputation; prevent fintech/banking licenses from being revoked due to AML control failures
• Market stability: Contribute to global financial system integrity by preventing illicit capital flows and asset theft

AML compliance is grounded in advanced statistical techniques, machine learning, and network science. Key foundations and recent advancements include:

• Rules-Based Transaction Monitoring: Foundational layer for scenario and threshold-based alert generation
• Supervised Machine Learning: Gradient boosting and deep learning for classification of suspicious vs. normal transactions; reduces false positives dramatically
• Unsupervised Anomaly Detection: Isolation Forests, Autoencoders, Variational Autoencoders detect novel typologies and emerging schemes
• Graph Neural Networks (GNNs): Model financial networks; detect fraud rings, shell company structures, and money laundering chains through link prediction and community detection
• Network Resilience Analysis: Analyze money laundering network topology; identify key nodes (nodes critical for illicit flows) for targeted intervention
• Explainable AI (XAI): SHAP, LIME provide transparent explanations for AML alerts; support investigations and regulatory inquiries
• Continuous Learning: Real-time model adaptation to emerging typologies, regulatory changes, and adversarial evasion techniques
• Multi-Channel Integration: Combine transaction data, KYC, sanctions lists, news, beneficial ownership registries, and external intelligence sources
• Adversarial Robustness: Develop models resilient to intentional obfuscation and layering techniques deployed by criminals

These advancements enable financial institutions to detect illicit activity with unprecedented accuracy and efficiency, staying ahead of evolving threats and regulatory demands.

We don't believe in one-size-fits-all and our solutions are tailored to your business problem. Our approach:

• Discovery: We assess your transaction volumes, customer base, regulatory landscape (FATF, FinCEN, regional rules), and existing AML/KYC infrastructure
• Architecture Design: We design end-to-end AML platforms integrating KYC onboarding, transaction monitoring, sanctions screening, network analysis, and reporting
• Technology Selection: We select ML models (boosting, deep learning), graph algorithms (GNNs), and tools optimized for real-time streaming and batch analytics
• Model Development: We build supervised models for transaction classification, unsupervised models for anomaly detection, and graph models for network fraud rings
• Rules & Typologies: We develop and implement scenario rules based on FATF typologies, internal typologies, and evolving criminal schemes
• KYC/CDD Automation: We integrate identity verification, document analysis, sanctions screening, beneficial ownership lookup, and continuous risk profiling
• Deployment & Integration: We deploy AML engines with APIs to core banking, CRM, payment systems, and regulatory reporting platforms
• Monitoring & Governance: We provide continuous model monitoring, alert performance tracking, and audit-ready documentation for supervisory examinations

Flexible Architecture and Deployment

• Cloud Deployment (AWS, Azure, GCP):
• Scalable infrastructure for processing billions of transactions in real-time
• Managed services for Kafka/streaming, ML inference, and graph databases
• Global compliance with data residency and encryption standards
• On-Premises Deployment:
• Full control over sensitive customer and transaction data
• High-performance computing for large-scale graph and streaming analytics
• Air-gapped environments meeting highest security classifications
• Hybrid Deployment:
• Customer data and core monitoring on-premises; ML training and analytics in the cloud
• Meets compliance and security requirements while leveraging cloud scalability

Phase 1: Assessment and Strategy:

• Audit your existing AML/KYC processes, transaction monitoring systems, and compliance infrastructure
• Define regulatory priorities (FATF, FinCEN, regional requirements), alert targets, and false positive reduction goals
• Design a comprehensive AML platform architecture incorporating machine learning, graph analytics, and real-time monitoring

Phase 2: Pilot Deployment:

• Pilot ML-based transaction monitoring on a subset of transactions; compare alerts against existing system
• Validate KYC onboarding workflows and sanctions screening against known test cases and regulatory expectations
• Develop alert dashboards, SAR generation templates, and investigator workflows for operational use

Phase 3: Production Integration:

• Deploy AML/KYC platform for all transaction streams, customer onboarding, and ongoing monitoring in real-time
• Integrate with core banking, payment, lending platforms; implement APIs for downstream systems and regulatory reporting
• Train compliance teams on interpreting ML alerts, conducting investigations, and maintaining audit trails for regulatory examinations

Phase 4: Continuous Monitoring and Optimization:

• Monitor alert performance, false positive rates, and true positive capture; optimize thresholds and model tuning
• Update typologies, rules, and models in response to new money laundering schemes, regulatory guidance, and typology sharing
• Expand AML capabilities to new products (crypto, correspondent banking), channels, and emerging risk domains (sanctions evasion, trade-based laundering)